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Historic timber structures face substantial fire loads and complex fire risks. Subsequent renovations and utilization may influence their fire safety performance. Therefore, accurately predicting indoor fire development in historic buildings and assessing their fire safety performance is crucial. Numerical fire simulation is currently at the forefront of analyzing and assessing fire risks in historic buildings. However, there is a shortage of globally accessible historic building fire data. This paper proposes a method to determine fire scenarios, peak heat release rates, and development curves of indoor fires in wooden historic buildings through a fire load investigation. Using the Guangzhou ancestral hall as an example, PyroSim fire dynamics simulation software is employed to calculate fire development and assess the available safe evacuation time. The simulation results are subsequently input into the Pathfinder evacuation simulation software to ascertain the required safe evacuation time for indoor occupants. A comparative assessment is conducted to evaluate the fire safety performance before and after the renovation of historic buildings. The research findings indicate that installing closed glass curtain walls in the courtyards of ancestral hall buildings in Guangzhou accelerates the infiltration of smoke during fires, leading to rapid fire spread and long-distance ignition, significantly reducing the time available for safe evacuation. Therefore, when renovating and utilizing the ancestral hall buildings in Guangzhou, the installation of ventilation and smoke extraction systems should be prioritized to slow down fire development. Additionally, controlling the number of indoor occupants is an effective management measure to mitigate fire damage in historic buildings. [ABSTRACT FROM AUTHOR]

The study of clan paintings reveals a shift in perspective from art aesthetics to cultural connotations to cultural identity, yet literature seldom discusses the relationship between art and kinship culture. Taking the murals of ancestral hall architecture in Guangzhou as an example, this paper utilizes text mining to identify factors influencing its decorative art. It reveals the traditional rites' artistic expression through dimensions of characters' demeanor and the transmission of content values, offering a fresh perspective for heritage value research. Findings: (1) themes and implications are mostly oriented towards positive value transmission, transitioning from idealistic layman life to the realism of lower-class existence, emphasizing humanization; (2) the extroverted portrayal of characters contrasts with the dignified, restrained etiquette of traditional rites, with some characters' portrayal and facial expressions exuding approachability; (3) murals conveying positive emotions are mostly related to longevity, auspiciousness, fortune, and heroic deeds, while those conveying negative emotions mainly involve elderly male figures, reflecting a content bias related to characters; (4) historical allusion murals with complex content reduce the emotional resonance and arousal efficiency of the viewer; (5) incomplete mural content increases negative emotions in perceivers, highlighting the impact of mural preservation on emotional resonance. To delve into the formation of clan painting art, it is essential not only to interpret the diversity of its patterns but also to demonstrate the representation of its social attributes in decorative art. The formation of clan painting decorative art exhibits kinship cultural attributes, epitomizing the essence of traditional ceremonial thought. [ABSTRACT FROM AUTHOR]

​​​​​​​As a crucial constituent of fine particulate matter (PM2.5), secondary organic aerosols (SOAs) influence public health, regional air quality, and global climate patterns. This paper highlights the use of hydrophilic interaction liquid chromatography (HILIC) which effectively retains strongly polar analytes that might exhibit incomplete or no retention in reverse chromatography, resulting in superior separation efficiency. A HILIC column was used to analyze six standards, environmental standards (1648a and 1649b), and samples collected in urban environments in Guangzhou in the Pearl River Delta region, which serve as valuable reference points for evaluating the organic composition of the atmospheric environment. The results indicate a high degree of accuracy in the analytical method. Sodium octyl- d17 sulfate serves as the internal standard, with a linear correlation coefficient of the six standards, boasting a linear correlation coefficient r ranging from 0.993–0.9991 and a slope, k , of the linear equation from 0.966–1.882. The instrument detection limits (IDLs) are established at 0.03–0.20 µgmL-1 , while the method detection limits (MDLs) fall within the range of 0.30–1.75 ngm-3 , demonstrating the method's exceptional sensitivity. Since isoprene-derived organosulfates (iOSs) are highly polar due to containing a hydrophilic bond to the hydroxyl group and a hydrophobic bond to the sulfate, and as such showed strong retention using this method, this technique employs sodium ethyl sulfate and sodium octyl sulfate standards for semi-quantitative compound analysis of iOSs. The error in sample analysis (EA) ranged from 12.25 %–95.26 %, and the two standards maintained a consistent recovery rate between 116 %–131 % and 86.4 %–127 %. These findings indicate a high level of precision when semi-quantifying compounds with similar structural characteristics, affirming the analysis method's minimal relative error and underscoring its repeatability, process stability, and the reliability of its results for iOSs. To enhance the method's reliability assessment, the study analyzed polar organic components of standard particulate matter samples (1648a and 1649b), providing precise determinations of several iOSs using this method. Methyltetrol sulfate (m/z 215, C5H11SO7-) is the highest concentration in the ambient samples, up to 67.3 ngm-3 in the daytime. These results serve as valuable reference points for assessing the organic composition of the atmospheric environment. [ABSTRACT FROM AUTHOR]

Accurate and fast traffic prediction is the data-based foundation for achieving traffic control and management, and the accuracy of prediction results will directly affect the effectiveness of traffic control and management. This paper proposes a new spatiotemporal periodic polynomial model for road traffic, which integrates the temporal, spatial, and periodic features of speed time series and can effectively handle the nonlinear mapping relationship from input to output. In terms of the model, we establish a road traffic speed prediction model based on polynomial regression. In terms of spatial feature extraction methods, we introduce a maximum mutual information coefficient spatial feature extraction method. In terms of periodic feature extraction methods, we introduce a periodic trend modeling method into the prediction of speed time series, and effective fusion is carried out. Four strategies are evaluated based on the Guangzhou road speed dataset: a univariate polynomial model, a spatiotemporal polynomial model, a periodic polynomial model, and a spatiotemporal periodic polynomial model. The test results show that the three methods proposed in this article can effectively improve prediction accuracy. Comparing the spatiotemporal periodic polynomial model with multiple machine learning models and deep learning models, the prediction accuracy is improved by 5.94% compared to the best feedforward neural network. The research in this article can effectively deal with the temporal, spatial, periodic, and nonlinear characteristics of speed prediction, and to a certain extent, improve the accuracy of speed prediction. [ABSTRACT FROM AUTHOR]

The relationship between social factors and the formation of exported mug designs is blurred. This study addresses how they influence material design. Based on a quantitative typological analysis, this paper interprets the cultural relationships underlying the evolution of mug designs exported during the Ming and Qing dynasties. The study reveals: (1) the typology and handle styles of mugs can be categorized into six types, with a predominance of Cylindrical bodies and Ear-shaped handles. Notably, artistic emphasis is concentrated on Bulbous cup bodies and Tail outward curved handles; (2) the design of mugs in the eighteenth century exhibited diversity, morphological similarities, and feature continuity, evolving from representational (Ming Dynasty) to abstract and then to minimalistic styles (Qing Dynasty), particularly evident in the three-dimensional modeling of handles for ergonomic comfort (transitioning from a singular Outward curved form to Ear-shaped form, and then to Entwined branch form); (3) the body shape of mugs transformed from Arc-barrel bodies to Bulbous bodies (focusing on heat retention), and eventually to Cylindrical bodies (prioritizing heat retention, practicality, and cost-efficiency).; (4) the volume of mugs steadily increased from the early eighteenth century, generally classified into large volume (≥ 500 ml), medium volume (200-499 ml), and small volume (< 200 ml); (5) considering the extreme range of volume (11090 ml) and height (25 cm), it indicates that these two variables do not necessarily have a direct positive correlation; (6) the low center of gravity in handle design (average width of 3.4 cm against an average height of 9.9 cm for the body) reflects considerations for the distribution of liquid weight and operational convenience. The volume of sample-10 (11127 ml) notably exceeds the typical range for tea-drinking utensils, revealing the mechanism of wealth and status symbolism. Market demand orientation emerged as the primary driver for the evolution of export mug designs during the Ming and Qing Dynasties, with adjustments in the merchant system playing a secondary role. The transformation in mug design reflects the interplay of multiple factors, with the trend toward minimalist design being a response to market demands. [ABSTRACT FROM AUTHOR]

Recent advances in knowledge graphs show great promise to link various data together to provide a semantic network. Place is an important part in the big picture of the knowledge graph since it serves as a powerful glue to link any data to its georeference. A key technical challenge in constructing knowledge graphs with location nodes as geographical references is the matching of place entities. Traditional methods typically rely on rule-based matching or machine-learning techniques to determine if two place names refer to the same location. However, these approaches are often limited in the feature selection of places for matching criteria, resulting in imbalanced consideration of spatial and semantic features. Deep feature-based methods such as deep learning methods show great promise for improved place data conflation. This paper introduces a Semantic-Spatial Aware Representation Learning Model (SSARLM) for Place Matching. SSARLM liberates the tedious manual feature extraction step inherent in traditional methods, enabling an end-to-end place entity matching pipeline. Furthermore, we introduce an embedding fusion module designed for the unified encoding of semantic and spatial information. In the experiment, we evaluate the approach to named places from Guangzhou and Shanghai cities in GeoNames, OpenStreetMap (OSM), and Baidu Map. The SSARLM is compared with several classical and commonly used binary classification machine learning models, and the state-of-the-art large language model, GPT-4. The results demonstrate the benefit of pre-trained models in data conflation of named places. [ABSTRACT FROM AUTHOR]

Efficient monitoring of water quality parameters (WQPs) is crucial for environmental health. Drone hyperspectral images have offered the potential for the flexible and accurate retrieval of WQPs. However, a machine learning (ML)-based multi-process strategy for WQP inversion has yet to be established. Taking a typical urban river in Guangzhou city, China, as the study area, this paper proposes a machine learning-based strategy combining spectral preprocessing and ML regression models with ground truth WQP data. Fractional order derivation (FOD) and discrete wavelet transform (DWT) methods were used to explore potential spectral information. Then, multiple methods were applied to select sensitive features. Three modeling strategies were constructed for retrieving four WQPs, including the Secchi depth (SD), turbidity (TUB), total phosphorus (TP), and permanganate index (CODMn). The highest R2s were 0.68, 0.90, 0.70, and 0.96, respectively, with corresponding RMSEs of 13.73 cm, 6.50 NTU, 0.06 mg/L, and 0.20 mg/L. Decision tree regression (DTR) was found to have the potential with the best performance for the first three WQPs, and eXtreme Gradient Boosting Regression (XGBR) for the CODMn. Moreover, tailored feature selection methods emphasize the importance of fitting processing strategies for specific parameters. This study provides an effective framework for WQP inversion that combines spectra mining and extraction based on drone hyperspectral images, supporting water quality monitoring and management in urban rivers. [ABSTRACT FROM AUTHOR]

Predicting carbon prices is crucial for the growth of China's carbon trading industry. This paper proposes a residual correction model that considers multiple influencing factors. First, the best historical data and main external factors input by the model are determined by using the partial autocorrelation function and Spearman correlation analysis, and the carbon price forecasting index system is constructed. Second, the whale optimization algorithm (WOA) is utilized to determine the optimal parameters of the extreme gradient boosting (XGBoost), and the WOA‐XGBoost model is built to perform preliminary carbon price forecasts and obtain the residual series. Finally, the carbon price residual series undergoes decomposition into multiple components utilizing the complete ensemble empirical mode decomposition for subsequent forecasting and the aggregation of outcomes. Experiments are conducted to predict two carbon trading markets in Hubei and Guangzhou, and a feature importance analysis is performed. The results indicate that the proposed hybrid model consistently outperforms the comparative models in terms of prediction accuracy. Furthermore, it is revealed that historical carbon prices and European Union carbon prices are the key factors influencing the prediction of carbon market prices. [ABSTRACT FROM AUTHOR]

Guangzhou is located in the southern part of China, near the South China Sea, belongs to Guangdong Province. It serves as China's southern gateway to the world and was historically called "The Southern Treasure of the Emperor", playing a crucial role in China's maritime trade history. Throughout Chinese history, Guangzhou underwent several cycles of "closure" and "opening", policies that severely impacted trade. However, foreign merchants never ceased to request permission from the Chinese government to engage in trade. In 1757, Emperor Qianlong decreed Guangzhou as the only one official port for foreign trade. This edict led to the establishment of the one port trading system in Guangzhou, as the Qing government aimed to counter foreign influences and local uprisings while maintaining a self-sufficient small-scale agricultural economy. Western nations and their traders sought opportunities within this system, aspiring to expand trade to other regions of China, even the inland areas. The exclusive status of Guangzhou as China's only one trading port persisted until 1842, following the defeat of the Qing government in the Opium War against Britain, the Nanjing Treaty was signed. This treaty opened Guangzhou, Shanghai, Ningbo, Fuzhou, and Xiamen as trading ports, bringing an end to the one port trading system. The reasons and impacts of the establishment of this system have not been comprehensively analyzed to date. Therefore, this paper based on historical archives, analyzes both the direct and underlying reasons behind Emperor Qianlong's decision and elucidates the system's diverse impacts on various aspects of Qing-era China. [ABSTRACT FROM AUTHOR]

As a crucial constituent of fine particulate matter (PM2.5), secondary organic aerosols (SOA) influence public health, regional air quality, and global climate patterns. This paper highlights the use of Hydrophilic interaction liquid chromatography (HILIC) which effectively retains strongly polar analytes that might exhibit incomplete or no retention in reverse chromatography, resulting in superior separation efficiency. A HILIC column was used to analyze 7 standards, environmental standards (1648a and 1649b), and samples collected in urban environments in the Pearl River Delta region of Guangzhou. That serve as valuable reference points for evaluating the organic composition of the atmospheric environment. The results indicate a high degree of accuracy in the analytical method, sodium octyl-d17 sulfate serves as the internal standard, with a linear correlation coefficient of the 7 standards, boasting a linear correlation coefficient R ranging from 0.987-0.999 and a slope, k, of the linear equation from 0.9662-2.2927. The instrument detection limit (IDLs) is established at 0.0026-0.0300 µg mL-1, while the method detection limit (MDLs) falls within the range of 0.0077-0.2300 ng m-3, demonstrating the method's exceptional sensitivity. Since isoprene sulphates are highly polar due to containing a hydrophilic bond to the hydroxyl group and a hydrophobic bond to the sulphate, and as such showed strong retention using this method. This technique employs Sodium ethyl sulfate and Sodium octyl sulfate standards for semi-quantitative compound analysis isoprene-derived OSs, the error in sample analysis (EA) ranged from 12.25-95.26% and the two standards maintaining a consistent recovery rate between 116%-131% and 86%-127%. These findings indicate a high level of precision when semi-quantifying compounds with similar structural characteristics, affirming the analysis method's minimal relative error and underscoring its repeatability, process stability, and the reliability of its results for isoprene OSs. To enhance the method's reliability assessment, the study analyzed polar organic components of standard particulate matter samples (1648a and 1649b), providing precise determinations of several isoprene OSs using this method. Methyltetrol sulfate (m/z 215) is the highest concentration in the ambient samples, up to 67.33 ng m-3 at daytime. These results serve as valuable reference points for assessing the organic composition of the atmospheric environment. [ABSTRACT FROM AUTHOR]

In recent years, promoting regional synergistic development has become the shared objective in city-regions during the new stages of development. This paper aims to uncover the dynamics of synergistic development in urban agglomerations by constructing an index measurement system based on four dimensions: industrial synergy, innovation synergy, infrastructure synergy, and institutional synergy. By comprehensively employing theories and methods such as "space of flow" big data analysis, network analysis, and interlocking network models, this study examines patterns and capacities of synergistic development in the Guangdong-Hong Kong-Macao Greater Bay Area. The main instances of industrial cooperation between cities are observed in Hong Kong-Shenzhen and Guangzhou-Shenzhen; capital flows primarily concentrate in Guangzhou-Shenzhen and Hong Kong-Shenzhen. Research paper and patent collaborations between cities are predominantly concentrated in Hong Kong-Shenzhen; joint laboratories are mainly established in Hong Kong-Macao and Hong Kong-Guangzhou; inter-city co-construction of higher education institutions is mainly focused on Guangzhou-Foshan and Shenzhen-Hong Kong. Infrastructure synergistic development performs best in Guangzhou-Shenzhen, Guangzhou-Foshan, and Shenzhen-Dongguan. Cross-border collaboration benefits from institutional advantages held by Hong Kong and Macao. Urban interactions with outstanding performance include Guangzhou-Shenzhen, Hong Kong-Shenzhen, Guangzhou-Foshan, Shenzhen-Dongguan. Additionally, increasing interaction trends can be observed between Guangzhou-Dongguan as well as Shenzhen-Zhuhai/Shenzen-Foshan partnerships. Understanding the typical characteristics and potential trends of synergetic development within the GBA holds significant practical value for establishing a first-class bay area along with a world-class urban agglomeration. • Concept and connotation of synergistic development in urban agglomeration area was examined. • Indicators and methods for evaluation of synergistic development in the GBA were conducted. • Patterns and capacities of synergistic development at urban scale in the GBA were explained. • Urban interaction with highest synergistic development level is Guangzhou-Shenzhen. [ABSTRACT FROM AUTHOR]

The train generates regenerative braking energy (RBE) during braking. When the RBE generated by the braking train is too large to be absorbed and utilized, the OCS voltage will rise rapidly. It may even exceed the rated voltage threshold, resulting in hidden dangers for driving safety. How to use RBE efficiently and reasonably has become a hot issue in recent years. This paper presents a metro timetable optimization method for multi-train operation to improve the RBE utilization efficiency of trains. Firstly, an energy-saving multi-objective model for multi-train operation is established, which takes the total energy consumption and travel time as the objective functions and the dwell time and departure interval as the constraints. Then, the genetic algorithm (GA) is used to generate the composition sequence of dwell time and departure interval, and the multi-node nonlinear model is used to calculate the total energy consumption of the metro line. Finally, Guangzhou metro line 7 is selected as an example to illustrate the effectiveness of the developed optimization model. The results show that the energy consumption of the whole line can be effectively reduced by adjusting the dwell time and departure interval. Meanwhile, the timetable optimization model with GA has better energy-saving effects by comparative analysis. • A metro timetable optimization method based on improved genetic algorithm is developed. • The multi-node nonlinear model is developed to calculate the total energy consumption. • The developed optimization model has better energy-saving effects than previous methods. [ABSTRACT FROM AUTHOR]

The energy crisis caused by climate change is the most significant challenge of the 21st century. Single-objective and multi-objective optimization studies for buildings often only consider the building itself, without considering the impacts of climate change. However, in the future, energy-efficient design and appropriate operating parameters will be crucial considerations in the face of global climate change. This paper aims to discuss the impact of climate change in the humid-hot region over the past 60 years on the energy consumption of office buildings and propose countermeasures. Firstly, the meteorological data of Guangzhou from 1960 to 2020 is statistically analyzed, and four typical meteorological years and corresponding air conditioning design days are established. Secondly, Energyplus is used to simulate the building's energy consumption, and the energy consumption factors are analyzed for sensitivity using jEplus. Finally, a multi-objective optimization is performed using jEPlus + EA with the Non-dominated Sorting Genetic Algorithm (NSGA-II). The results found that climate change led to a significant increase in the duration of high outdoor temperatures during summer. Specifically, the daily dry-bulb temperature for air conditioning design increased by 0.4–0.7 °C, and due to climate change, the energy consumption of the office building increased by about 4.6 %. The main factor affecting the cooling energy consumption in summer was the indoor cooling set point (8.29%–30%), infiltration air volume (1.0%–8.6%), fresh air volume (3.2%–16.06%), the glazing solar transmission rate (1.25%–9.0%), building orientation (0%–1.43%), and relatively small influence of envelope insulation performance. The operation scheme determined by multi-objective optimization can reduce the cooling energy consumption of the building by 10.6%–16.8%, which can mitigate the rise in energy consumption resulting from climate change to a greater extent. [Display omitted] • Discuss the impact of climate change over the past 60 years on the energy consumption of office buildings. • There was a significant increase in the duration of high outdoor temperatures during the summer. • Multi-objective optimization is performed using jEPlus + EA. • Climate change caused energy consumption to increase by about 4.6 %. • Optimal solutions can reduce the cooling energy consumption of the building by 10.6%–16.8%. [ABSTRACT FROM AUTHOR]

As the financialization of carbon emission right, developing effective carbon asset trading strategy is important for both investors and regulators. Traditional trading strategy based on technical analysis is hardly profitable due to high complexity inherent in carbon markets. In order to increase investment returns and reduce risk in carbon market, this paper introduces PSO-VMD-DRL, an innovative carbon asset trading strategy that integrates signal decomposition technology and deep reinforcement learning (DRL). Firstly, the carbon price is decomposed into multiple components by variational mode decomposition (VMD) optimized through particle swarm optimization (PSO) to extract frequency features and reduce noise. Then state representation of the environment (state space) is constructed based on the components and transaction data. Finally, deep reinforcement learning (PPO, A2C, DDPG) are performed separately based on state space to learn optimal trading strategy. Empirical studies based on the data from carbon markets of Hubei and Guangzhou validate that PSO-VMD-DRL can adapt to carbon asset with the better profitability and risk resistance. The PSO-VMD-DRL outperform the other comparison strategies with superiority in all evaluation indices, achieving annual return of 21.39%, 17.67%, 25.50% and 46.34%, 44.55%, 43.03% in carbon market of Hubei and Guangzhou. [ABSTRACT FROM AUTHOR]

For small radius curve shield tunneling, over excavation is required on the inner side of the curve to corner the shield machine. Currently, there is no corresponding analytical solution for horizontal displacements. In this paper, using the image method and Mindlin solution and considering four factors: additional thrust on the excavation surface, frictional resistance of the shield shell, ground loss, and grouting pressure at the shield tail, a formula for calculating stratum horizontal displacements caused by small radius curve shield tunneling is derived. Based on the 220 kV Rhinoceros Station cable shield tunnel project in Guangzhou, the stratum horizontal displacement law and influencing factors are analyzed. Through comparison and verification with on-site measured data, the results indicate that the theoretical formula derived is reasonable and reliable, providing a new approach for predicting the stratum horizontal displacements induced by curve shield tunneling. The law of stratum horizontal deformation exhibits that the horizontal displacement caused by the over excavation side of the curve moves toward the tunnel side. The outer side of the curve shifts away from the tunnel. The peak appears near the axis of the shield tunnel. The ground loss has the most significant influence on stratum horizontal displacements. Furthermore, on the same tunnel cross-section, the closer the soil is to the tunnel, the greater the horizontal displacement of the soil, and vice versa. The change in shield shell length profoundly affects horizontal displacements. The friction difference coefficient has a significant impact on the stratum horizontal displacement outside the curve. [ABSTRACT FROM AUTHOR]